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TONet: A Fast and Efficient Method for Traffic Obfuscation Using Adversarial Machine Learning
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DOI：10.1109/LCOMM.2022.3195685.png)
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摘要
En 中文
In this letter, we address the problem of privacy leakage in communications based on analysis of traffic patterns. We propose an efficient method of traffic obfuscation based on neural networks, that generates traffic distortions with minimal overhead and computational cost. Our experimental results show that the proposed method is orders of magnitude faster in implementation and has a higher obfuscation success rate with less perturbation on the traffic samples, compared to previously proposed adversarial machine learning-based traffic obfuscation methods.
Keyword:
Perturbation methods
Privacy
Delays
Cryptography
Analytical models
World Wide Web
Standards
Traffic type obfuscation
adversarial learning
security
privacy leakage
期刊
IF:
4.4
论文数:
1.3W
被引数:
2.2W
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